Count Love

To improve how Count Love documents protests, we've recently made two substantial changes to our dataset. First, we've modified the tags that we assign to protest events to explicitly record three types of information: the categories that the protest belongs to in blue, specific positions for or against a particular topic in orange, and important details in gray. We're labeling new events in this manner, and we've retroactively applied these labels to our past data. Second, we've exported all protest events that we've found on our search page, including events that we don't show by default on our homepage (for example, protests about labor contract negotiations). We hope that these more detailed labels and events data enable deeper analyses of protestor concerns for activists, researchers, journalists, and policy makers.

We started implementing the new tagging system and relabeling past data five months ago. As other organizations have begun to use our data, and as we've started publishing our own claims in other venues, in the interest of transparency and replicability we realized that we needed more explicit and flexible data labels. In the past, our labels had implicit meanings—for example, “racial injustice” protest events were always protests about Civil RightsFor racial justice. However, this implicit meaning made it difficult to easily show data about other racial injustice protests that we don't show by default, such as protests in support of Confederate monuments or for white supremacy. Additionally, previously we could only attach one category and one protest position for each event. This restriction limited our ability to fully document cross-category protests, such as the Charlottesville protests in August 2018 that were for racial justice and against the president. Our new tagging system allows us the flexibility to describe these protests as Civil RightsExecutiveAgainst presidentFor racial justiceCharlottesville. By reusing explicit labels to describe every protest, we can compare connections between those labels to explore broader similarities and differences between all of the protests we've learned about. (Labeling data in this manner also opens up machine learning opportunities for training and prediction. We have built tools based on these tags to help with our nightly crawls, but we will save that discussion for another blog post.)

As an example of the additional nuance that we can now use to describe and analyze protests, as of December 26, 2018, the most common category/protest position tags are:

Against firing/reassignmentAgainst president (192 coincident occurrences)

Based on these trends, between January 20, 2017 and December 26, 2018, protestors expressed substantial desires for greater gun control and compassionate immigration reform, as well as concerns about ending the Mueller investigation, against white supremacy, and against police brutality. Interestingly, while there have been several national protests related to guns, immigration, and the president, thus far we haven't documented any national protest efforts against racially motivated police brutality. The prominence of these protests despite the lack of corresponding nationally organized events starkly highlights that racially motivated police brutality remains a substantial societal/policy failure for a subset of marginalized Americans.

The protest trends that we've just explored based on analyzing the most frequent tag pairs that appear in Count Love's data only represent a topical exploration of the types of questions that we hope to ask and answer about protests and protestor concerns. To help visualize other trends across our dataset, in the interactive graph below, we counted all occurrences of tags that we've assigned to protests for a more compassionate country and created a force directed graph that maps all connected tags together. Each node represents one tag. Nodes naturally try to push other nodes away; however, tags that appear frequently together will draw those respective nodes closer to one another. Tags that appear frequently in the dataset are represented as larger nodes, and pairs of tags that appear together frequently are connected by thicker lines. When the forced directed graph reaches an equilibrium, large nodes in the middle of the graph will tend to represent the most prominent protest positions that span multiple categories, while nodes near the edge of the graph will tend to represent protest topics that have less in common with other protests to date. We encourage you to explore the graph by mousing over the different nodes to learn about what Americans have been protesting. And if you find that your exploration takes you somewhere interesting, please feel free to keep exploring with our full protest dataset!